Visual Dynamic Model Inspecting with OPM Model-Based Simulation Environment

Yevgeny Yaroker,Valeria Perelman,Prof. Dov Dori

April 21, 2023

Introduction Our domain: Conceptual design phase in

the systems engineering lifecycle. The decisions made during this phase

are the most critical to get right and hardest to change.

Existing testing approaches operate on the system’s detailed design, a stage which is too technical for the customer to follow may be very expensive to backtrack

Motivation There is a need to detect problems starting at

early stages of the system development, when detailed design does not yet exist.

Benefits include: Debugging during earlier stages of system

development, finding system malfunctions before investing in extensive code writing

Working on a more abstract model – keeping the model simpler with concealed details.

Modeling with focus on satisfying requirements. Predictability of changes/modifications in design

Model-Based Simulation Frameworks System model simulation

frameworks for detailed design: Modelica, ARENA, Simulink, …

Graphical presentation of process flows OPM Animation (OPCAT)

Model-based simulation case tools: xUMLite – (xUML) …

run-time verification

unit tests integration tests

formal verification

questionnaires

Conceptual modeling

Time/Phase

checklistscenario

simulation

metrics

DetailedDesign

Sub-systemsImplementation

Integration

System testing and evaluation approaches

Non-formal

Formal

Conceiving & alternatives evaluation

prototyping

What makes the conceptual model evaluating so complicated?

Human abilities to comprehend system dynamics, based on numerous static diagrams, are limited even when well-organized and holistic modeling languages (ML) are used.

High level of abstraction, which is typical of conceptual MLs, propagate notations ambiguity.

These factors lead to numerous human errors while: constructing system models reading conceptual models written by others

Research Goal

Develop and evaluate a visual dynamic model inspecting with OPM model-based simulation environment.

What is Object Process Methodology (OPM)?

OPM is a comprehensive, generic systems development and lifecycle support paradigm

OPM Integrates the system’s function, structure and dynamics in a single, unifying model.

Complexity is controlled through recursive and selective scaling (zooming) of objects and/or processes to any desired level of detail.

The OPM model combines image and text: intuitive graphics a subset of natural English

OPM Basic Concepts

Object: A thing that exists or can exist physically or logically

Process: A thing that transforms an object by creating it or consuming it or changing its state

Objects and processes can be connected with links, which can be structural (such as aggregation, generalization) and procedural (enabling, transformation, and event links)

10

OPM main links

Structural Links Procedural linksConnect objects to objects Connect objects to processes

Aggregation-Participation

Generalization-Specialization

Exhibition-Characterization

Classification-Instantiation

Consumption

Result

Effect

Agent

Uni- and Bi-directionalTagged link

Instrument

object object

Solution Outline Develop a model-based animation mechanism

as an extension of OPM using the infrastructure provided by OPCAT

Define clear system behavioral rules in line with OPM syntax and semantics

Design a software tool with a wide user profile: having easy-to-use User Interface (UI), not requiring a special technical background providing efficient problem detection and reporting

mechanisms Develop a flexible system to enable future

extensions

Technical Requirements Simulation/visualization workflow requirements:

Simulation shall follow OPM rules. Instance-level simulation shall be enabled.

“DVD film” simulation mode requirement: Enable easy work through possible scenarios inexperienced users:

Forward/backward simulation, step by step/continuous simulation, pause/continue (relevant only for continuous mode), changing simulation speed

Debugging functional requirements: Capability to define breakpoints Provide “lifespan” component which graphically describes the state of all the

OPM entities at any stage of simulation Provide special “Debug Info” component which detects possible problems

and notifies user about them. Capability to reproduce problematic scenarios

Simulation Typical View

Process is colored if it is currently executed

Object with existing

instance is colored

Red token runs on the activated

link

Simulation Main Controls Main Toolbar – Controlling simulation flow

Starting/Stopping simulation. Playing forward/backward. Controlling simulation velocity. Invoking simulation properties dialog.

Status Bar – Observing simulation status Play mode. Current timeline.

Main toolbar

Status Bar

Model Debugging Process

User can toggle breakpoint on a process. The simulation runs till the breakpoint is

reached. Reaching the breakpoint will pause the

simulation.

Debug Information

Notifying a user regarding the problems making the further progress run impossible, such as: A process invocation failure, A required manual process activation, “No future simulation events” situation.

Architecture – Main Constructs

Task Queue consists of Simulation Tasks. A Simulation Task:

represents an atomic simulation activity implements Command and Undo DPs.

Task Queue keeps the simulation working schedule.

Rule defines the Simulation Tasks to be scheduled upon some simulation events.

Scheduler uses rules to build the Tasks Queue.

Architecture: The Three Layered Model

Tasks Queue Creation Layer

Tasks Queue Execution Layer

Plug-in Layer

Rules

Simulation Rule is defined for each OPM entity and for each simulation event. For instance, there is a rule for object’s

instance creation and its deletion. An activated rule determines the

Simulation Tasks to be executed and the set of next Rules to be scheduled.

For each rule there is a class implementing its functionality.

Process Activation Rule Example

Activation Conditions: The elements linked to the process with one of the

following links should be active - Instrument link. Condition link. …

Consequent Rules: Process termination will be scheduled t time units after its

activation time, where t – is the process execution time. Elements linked to the process with Consumption link will

be deactivated. …

Consequent Tasks: The process will be painted. Elements linked to the process with Result link will be

painted progressively. …

Evaluation Experiment

Group A Group B

SystemModel

1

Static (manual) analysis Analysis with the simulation environment assistance

SystemModel

2

Analysis with the simulation

environment assistance

Static (manual) analysis

Two OPM models were prepared for two different example systems.

The systems are very similar in their size and complexity. Structural and behavioral errors were inserted intentionally to

the OPM models. Two groups of students were asked to find all the errors in the

two models. Each group analyzed one system using solely static set of the

model diagrams, and another system using simulation tool. Following table describes the experiment setup

Evaluation Results (1) Group of 98 students made the experiments in

pairs (49 pairs). The experience of this group with OPM and

system modeling is average.

Behavioral errors found

(max = 5)

Structural errors found

(max = 5)

Static (manual)analysis

2.12 1.08

Analysis with theOPCAT simulation

environment

2.98 0.50

Evaluation Results (2)

The students were also asked to give their general impression about helpfulness of the tool. The average mark is 4.81 on a 1-7 scale.

T P

Behavioral aspect 5.66 < 0.001

Structural aspect -3.77 < 0.001

Paired t-test results. N =49 pairs, 98 students.

Summary

Through the research visual dynamic model inspecting with OPM model-based simulation environment was developed.

Results gathered using two experiments carried out on a large group of students confirmed the efficiency of the proposed solution.

Although the proposed solution is OPM-oriented, the architecture and attitude could be reused to implement simulation engines for other MLs